基于交互数据设计机器人全身触觉感知，提升社交触感识别能力。
Requirement-Driven Design of Whole-Body Social Tactile Sensing via Virtual Human-Robot Interaction
- 从真实交互数据推导触觉传感器的布局与分辨率需求
- 构建5520次实验的公开触觉数据集，覆盖9种社交触摸动作
- 方法可迁移至不同机器人形态，指导硬件设计前的需求定义
社交物理人机交互中的触觉感知通常依赖硬件预设配置，限制了感知范围与手势识别能力。本文提出一种需求驱动框架，直接从交互数据中推导触觉感知的空间分辨率与布置要求。通过结合触觉反馈的VR平台，在多种社交场景下采集高分辨率全身接触分布数据，识别出九种常见社交触摸动作。选取其中八种动作，对18名参与者进行受控数据采集，构建了一个包含5520次试验的开源数据集。通过对接触分布与模拟触觉编码的分析，为类人机器人平台提供了皮肤覆盖与传感器密度的定量基准。该方法虽在单一机器人平台上验证，但具备可迁移性，可用于其他机器人形态，实现在硬件制造前确定形态特异性感知需求。
原文摘要 · Abstract (English)
Tactile sensing for social-physical human-robot interaction (spHRI) is designed in a hardware-driven manner, where predefined sensor configurations constrain coverage, spatial resolution, and the range of recognizable gestures. We propose a requirement-driven framework that derives sensing requirements, specifically spatial resolution and placement, directly from interaction data. Using a VR-based platform with haptic feedback, we collected high-resolution whole-body contact distributions across multiple social scenarios, from which we identified nine recurring social touch gestures. Eight gestures were selected for controlled data collection with 18 participants, yielding an open-source dataset of 5,520 trials. Analysis of contact distributions and simulated tactile encodings provides quantitative baselines for skin coverage and sensor density on a humanoid robot platform. While demonstrated on a single robot platform, the methodology is designed to be transferable to other robot morphologies, potentially enabling morphology-specific sensing requirements to be derived prior to hardware fabrication.
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